1 / 29

Traditional Systems Engineering

Traditional Systems Engineering. (Is REALLY Model Based Systems Engineering). Kenneth A. Lloyd, Jr. Objectives of this Presentation. Provide background context & research for SE? Raise awareness of models in SE practice. SE Conops are models. SE Requirements are models.

roana
Download Presentation

Traditional Systems Engineering

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Traditional Systems Engineering (Is REALLY Model Based Systems Engineering) Kenneth A. Lloyd, Jr.

  2. Objectives of this Presentation • Provide background context & research for SE? • Raise awareness of models in SE practice. • SE Conops are models. • SE Requirements are models. • SE Validation and Verification are models. • All SE Documents map to models. • Show Concepts are the foundation of models. • Are Systems Models? • No, but you can model systems. • Have some fun …

  3. Background “Scientists [and engineers] come to their particular problem with an accepted body of knowledge behind them, and on which they expect to draw, without questioning the validity of each and every method, assumption, or set of facts that they use. If we all tried to work everything out from first principles, or even insisted on understanding every piece of the puzzle in equal detail, none of use would ever get anywhere. So to some degree we have to accept that whatever has been acknowledged by the relevant community has been done carefully and correctly, and can be relied on … But the process is far from perfect, and once in a while we are surprised to discover that a piece of knowledge we had long taken for granted is questionable or even wrong.” • Duncan J. Watts, from • “Six Degrees: The Science of a • Connected Age” [p. 132]

  4. Background • Experts notice features and meaningful patterns of information that are not noticed by novices. • Experts have acquired a great deal of content knowledge that is organized in ways that reflect a deep understanding of their subject matter. • Experts’ knowledge cannot be reduced to sets of isolated facts or propositions but, instead, reflects contexts of applicability: that is, the knowledge is “conditionalized” on a set of circumstances. • Experts are able to flexibly retrieve important aspects of their knowledge with little attentional effort. • Though experts know their disciplines thoroughly, this does not guarantee that they are able to teach others. • Experts have varying levels of flexibility in their approach to new situations. John D. Bransford, Ann L. Brown, and Rodney R. Cocking (eds.), How Experts Differ from Novices

  5. 4 Major Concerns of SE • Enterprise aspects • Technical aspects • Project aspects • Agreement aspects Brief Our focus

  6. Enterprise Aspects

  7. Models – Steve Lehar Model Concept Phenomenon Concepts do not need, nor do they have the same “topology” as reality. They have maps. Courtesy: Steve Lehar, PhD – Cognitive Science, Boston University

  8. Models – Roger Penrose Model Conceptual Phenomenon Roger Penrose – Road to Reality

  9. The Chasm – The Problem Domain Courtesy: Steve Lehar, PhD – Cognitive Science, Boston University

  10. Minimal Model System A Hypothesis or Theorem* ** Real world Phenomenon Idealized at equilibrium A statement of what you believe you know, and what you don’t

  11. In Mathematical Language F is called a functor

  12. Carnegie-Mellon Models Carnegie-Mellon University A System of Model Interaction Related to Phenomenon

  13. Familiar Ground - The SE ‘V’

  14. Evolutionary ‘V’

  15. Background - The SE ‘V’

  16. Models The Man Who Mistook His Brain For His Mind

  17. Models Aussi, ceci n’est pas un modéle INCOSE Handbook 3.1 p. 2.4

  18. Conceptual Hierarchy Higher Abstraction

  19. Alex Stepanov’s Concept Model

  20. Maps to Models Petri nets UML, SysML and Textual Documents Each level has elements of self-similarity – but not equality

  21. Technical Aspects Focus • Requirements definition, • Requirements analysis, • Architectural design, • Implementation, • Integration, • Verification, • Transition, • Validation, • Operation, • Maintenance, and • Disposal Focus Focus

  22. Zia - A Real-world Example

  23. Concept of Operations Zia goes here

  24. Overview Overview (excerpt) “Effective management and stewardship of the nuclear weapons stockpile into the future requires the ability to accurately assess the behavior of the weapons in order to ensure robust and reliable performance while maintaining the testing moratorium. These accurate assessments drive the requirements for predictive capability in weapons science, including a fine-scale numerical resolution and advanced models for physics and material behavior.” – pg. 5 Model? ? Model? Models?

  25. Models – Steve Lehar Redux Model What does this requirement mean? Concept Phenomenon as Measureable, Meaningful Data: Requirements Requirements do not need, nor do they have the same “topology” as reality. They have maps to models. Courtesy: Steve Lehar, PhD – Cognitive Science, Boston University

  26. Zia’s High Level Reqs.

  27. Mapping Information to Models Agents, spiders and crawlers … Oh, my!

  28. What does it all mean?

  29. Contact Info Kenneth A. Lloyd, Jr. Director, Systems Science Watt Systems Technologies Inc. Albuquerque, NM 87114 USA kenneth.lloyd@wattsys.com

More Related